flash flooding case study

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Understanding flash flooding in the Himalayan Region: a case study

Katukotta nagamani.

1 Centre for Remote Sensing and Geoinformatics, Sathyabama Institute of Science and Technology, Chennai, India

Anoop Kumar Mishra

2 Office of Director General of Meteorology, India Meteorological Department, Ministry of Earth Science, SATMET Division, Mausam Bhavan, New Delhi, 110003 India

Mohammad Suhail Meer

Jayanta das.

3 Department of Geography, Rampurhat College, PO- Rampurhat, Birbhum, 731224 India

Associated Data

The data that support the findings of this study are available from the corresponding author upon reasonable request.

The Himalayan region, characterized by its substantial topographical scale and elevation, exhibits vulnerability to flash floods and landslides induced by natural and anthropogenic influences. The study focuses on the Himalayan region, emphasizing the pivotal role of geographical and atmospheric parameters in flash flood occurrences. Specifically, the investigation delves into the intricate interactions between atmospheric and surface parameters to elucidate their collective contribution to flash flooding within the Nainital region of Uttarakhand in the Himalayan terrain. Pre-flood parameters, including total aerosol optical depth, cloud cover thickness, and total precipitable water vapor, were systematically analyzed, revealing a noteworthy correlation with flash flooding event transpiring on October 17th, 18th, and 19th, 2021. Which resulted in a huge loss of life and property in the study area. Contrasting the October 2021 heavy rainfall with the time series data (2000–2021), the historical pattern indicates flash flooding predominantly during June to September. The rare occurrence of October flash flooding suggests a potential shift in the area's precipitation pattern, possibly influenced by climate change. Robust statistical analyses, specifically employing non-parametric tests including the Autocorrelation function (ACF), Mann–Kendall (MK) test, Modified Mann–Kendall, and Sen's slope (q) estimator, were applied to discern extreme precipitation characteristics from 2000 to 201. The findings revealed a general non-significant increasing trend, except for July, which exhibited a non-significant decreasing trend. Moreover, the results elucidate the application of Meteosat-8 data and remote sensing applications to analyze flash flood dynamics. Furthermore, the research extensively explores the substantial roles played by pre and post-atmospheric parameters with geographic parameters in heavy rainfall events that resulted flash flooding, presenting a comprehensive discussion. The findings describe the role of real time remote sensing and satellite and underscore the need for comprehensive approaches to tackle flash flooding, including mitigation. The study also highlights the significance of monitoring weather patterns and rainfall trends to improve disaster preparedness and minimize the impact of flash floods in the Himalayan region.

Introduction

The most significant challenges affecting a country's long-term social, economic, and environmental well-being stem from natural disasters. This includes extreme hydro-meteorological events like cloudbursts and excessive rainfall, which, due to their severe complications and intensity, have become a focal point of research, particularly in mountainous areas. The exploration of these events is crucial for developing strategies to mitigate their impact for mountainous region 1 . In the Himalayan context, the discernment of topographical intricacies assumes paramount importance due to their potential rapid escalation into calamitous events 2 . Consequently, a comprehensive understanding of hydrological challenges and water resource resilience becomes imperative, as these phenomena manifest in diverse catastrophic forms 3 . To delineate and analyze these hydrological challenges and resilience, hydrological modeling emerges as a crucial tool. The efficacy of such modeling is contingent upon the utilization of high-resolution geospatial data, particularly within the Soil and Water Assessment Tool (SWAT) framework. This integration enhances precision in water resource management, addressing the intricacies posed by the challenging Himalayan terrain 4 . This aligns with the study of Patel et al. 2022 5 , who concentrate on the 2013 Uttarakhand flash floods, underlining the importance of hydrological assessments and the development of disaster preparedness strategies in the region. The catastrophic nature of flash floods caused by cloud bursts and landslides in mountainous regions is highlighted as the most devastating natural disaster 6 . Instances of such disasters precipitate multifaceted consequences, encompassing loss of life, infrastructural degradation, and disruption of financial operations. Mitigating these adversities necessitates the systematic monitoring and analysis of flood events. A historical examination underscores the pivotal role of floods, emerging as the foremost impactful natural calamity, with an annual average impact on over 80 million individuals globally over the past few decades. The substantial global impact, as evidenced by floods contributing to annual economic losses exceeding US$11 million worldwide 7 , is further underscored by the difficulty in collecting information on land use, topography, and hydro-meteorological conditions. Anticipating an increased frequency of precipitation extremes and associated flooding in Asia, Africa, and Southeast Asia in the coming decades, this challenge has prompted a debate on the necessary adaptations in flood management policies to address this evolving reality 8 . India, facing the highest flood-related fatalities among Asian countries 9 , 10 , encounters heightened vulnerability to disaster threats. This susceptibility is further exacerbated by the country's extensive geographic variability, making the development and implementation of a climate response strategy considerably more challenging 11 .

The Indian Himalayan Region, being crucial to the national water, energy, and food linkage due to its variety of political, economic, social, and environmental systems, is uniquely vulnerable to hydro-meteorological catastrophes, including floods, cloudbursts, glacier lake eruptions, and landslides 12 – 15 . During monsoon season the cloud burst is increasing in the Himalayan region.This phenomenon is closely tied to the unique climatic conditions prevalent in the Himalayas during this period. Monsoons in this region bring intense and sustained rainfall, characterized by the convergence of moisture-laden air masses, especially from the Bay of Bengal, attributing to landslides, debris flows, and flash flooding 16 . These result in significant loss of life, property, infrastructure, agriculture, forest cover, and communication systems 17 . In 2013, the Himalayan state of Uttarakhand experienced devastating floods and landslides due to multiple heavy rainfall spells 17 , 18 . On February 7th, 2021, a portion of the Nanda Devi glacier in Uttarakhand's Chamoli district broke off, causing an unanticipated flood 19 – 21 . During this sudden flood, 15 people were killed, and 150 went missing. These disasters have disrupted the Himalayan ecology in several states, including Uttarakhand, and the cause and magnitude of these disasters have been made worse by human activities, including building highways, dams, and deforestation 22 . When we check the flood record of Uttarakhand, Himalaya, the area has experienced catastrophes during 1970, 1986, 1991, 1998, 2001, 2002, 2004, 2005, 2008, 2009, 2010, 2012, 2013, 2016, 2017, 2019, 2020, and 2021, making them among the most significant natural disasters to have struck Uttarakhand 16 , 21 .

The rising trend of the synoptic scale of Western Disturbance (WD) activity and precipitation extremes over the Western Himalayan (WH) region during the last few decades is the result of human-induced climate change, and these changes cannot be fully explained by natural forcing alone. This phenomenon is observed over the large expanse of the high-elevation eastern Tibetan Plateau, where a higher surface warming in response to climate change is noted compared to the western side 22 , 23 . Since the Industrial Revolution, the Himalaya and the Tibetan plateau have warmed at an increased rate of 0.2 degrees each decade (1951–2014) 24 . In the Himalayan region, the mean surface temperature has increased by almost 0.5˚C during 2000–2014. This alteration in climate (temperature) has resulted in a decrease in the amount of apples produced in low-altitude portions of the Himalaya. The warming of the planet is directly responsible for these effects. The Himalayan region has experienced a decline in pre-monsoon precipitation towards the end of the century, leading to new societal challenges for local farmers due to the socioeconomic shifts that have taken place 25 . Simultaneously, there has been an increase in the highest recorded temperature observed throughout the monsoon season. In tandem with heightened levels of precipitation, an elevation in the maximum attainable temperature has the potential to amplify the occurrence of torrential rainfall events during the monsoon season 26 . This long-term change in atmospheric parameters, known as climate change, may affect river hydrology and biodiversity. The associated shifts in climate pose a significant risk to hydropower plants if certain climate change scenarios materialize. As part of this broader context, the dilemma of spring disappearance should be thoroughly analyzed to provide scientific, long-term remedies and mitigation strategies for potential hydrogeological disasters. This is crucial due to the observed increase in the frequency of landslides, avalanches, and flash floods in recent years 24 .

El Niño–Southern Oscillation (ENSO) and Equatorial Indian Ocean Oscillation (EQUINOO) play a crucial role in the teleconnection of India's Monsoon, as well as in determining rainfall patterns and the occurrence of flash floods across different regions of India. At a regional level, a study was conducted to examine the impact of various types of climatic fluctuations on the onset dates of the monsoon. Northern India, specifically northern northwest India, referred to as SR15, consistently experiences a delayed start to its seasons, regardless of the climatic phase 27 . The occurrence of significant anomalies in sea surface temperatures (SST) in the tropical Pacific region, associated with ENSO and EQUINOO, is accompanied by large-scale tropical Sea Level Pressure (SLP) anomalies related to the Southern Oscillation (SO) 28 , 29 . The Equatorial Indian Ocean Oscillation (EIO) represents the oscillation between these two states, manifested in pressure gradients and wind patterns along the equator (EQUINOO).

The negative anomaly of the zonal component of surface wind in the equatorial Indian Ocean region (60°–90°, 2.5° S—2.5° N) is the foundation for the EQUINOO index 30 . Additionally, they demonstrated that between 1979 and 2002, any season with excessive rainfall or drought could be "explained" in terms of the favorable or unfavorable phase of either the EQUINOO, the ENSO, or both. For instance, in 1994, EQUINOO was favorable, but ENSO was negative, resulting in above-average rainfall in India. Conversely, ENSO was favorable, EQUINOO was unfavorable between 1979 and 1985, and India saw below-average rainfall. They, therefore, proposed that by combining those two climate indices, it would be possible to increase the predictability of rainfall during the Indian monsoon. The quantity of rainfall throughout a storm event that might cause a significant discharge in a particular river segment is known as a "rainfall threshold" 31 , 32 . Different techniques, indicators, and predictor variables can be used to derive rainfall thresholds. There are four categories of methodology: empirical, hydrological/hydrodynamic, probabilistic, and compound approaches. Empirical rainfall thresholds are among the most popular methods for constructing EWS in local, regional, and national areas 33 – 35 . Empirical methods use historical flood reports and rainfall amounts to perform a correlation analysis linking the frequency of event to the amount and length of essential precipitation 36 – 38 . Several empirical rainfall threshold curves may be found in literature from various countries 32 , 39 – 41 . Although this research concentrated on various shallow landslides and mudflows, flash flood risk systems can be set using actual rainfall thresholds 42 . Similarly, the principles of the Flood Risk Guideline (FFG) method serve as the foundation for hydrogeological precipitation limits 30 , 41 , 43 , 44 . The fundamental concept of FFG is to use reverse hydrologic modelling to identify the precipitation that produces the slightest flood flow at the basin outlet. Alerts are sent out whenever the threshold is exceeded for a specific time for the real-time actual daily rainfall or the precipitation forecast. This method needs data on precipitation collected using radar or real-time rainfall sensors 45 , 46 . Other threshold approaches for rainfall, however, require the same data. The modelling of various synthetic photographs, regionally dispersed models, and the prior soil moisture status have all been incorporated into the FFG, which is widely used worldwide 46 . Hydraulic models have been developed recently, allowing the threshold to be determined by the canal design, features, and the link between the achieved water table and the inundated area 47 , 48 .

Recent flood events underscore the inadequacy of relying solely on structural safeguards for comprehensive protection against such catastrophes. The imperative for an effective flood management approach becomes paramount to preemptively mitigate these calamities and ensure sustainable safety measures. The present study generates rainfall product that uses real-time satellite data from Meteosat-8 to summarize the significant short-lived localised multiple rainfall events that result flash flooding in the Nainital, Uttrakhand, during October 2021 48 . This method was utilized to investigate the flood events over J&K 2014 49 . Rajasthan in 2019 50 and Bihar and Assam in 2019 51 . This study introduces a pioneering approach by precisely measuring the peak rainfall hours and correlating them with daily rainfall, elucidating their direct correlation with flash flooding in the study area. A distinctive feature of this research is its integration of time series rainfall data with socioeconomic metrics to underscore the significant damage caused by a major flash flood incident. The exploration of the role of sheer slope in flooding provides a unique angle to flood dynamics. Additionally, the study delves into pre-atmospheric parameters specific to the study area that played a pivotal role in initiating flash flooding. By shedding light on these intricate details, this study establishes itself as a trailblazer in disaster mitigation strategies, emphasizing its pivotal role in advancing our understanding of flash flood dynamics and fortifying disaster response frameworks.

The economic and climatic conditions of India are intricately linked to the region of Himalaya, renowned for its delicate ecosystems and geological intricacies 52 . Spanning a vast area, the Indian, Himalaya is among the recent mountain ranges on the surface of earth, marked by the study delves into the vulnerability of the region of Himalaya, examining the intricate interplay of geographical and atmospheric parameters in flash flood occurrences. The area has susceptibility to geological hazards, topographical nuances, biodiversity, and water resource dynamics 53 . Geographically positioned between latitudes 28.44° to 31.28°N and longitudes 77.35° to 81.01°E, with elevations ranging from 7409 to 174 m, Uttarakhand, depicted in Fig.  1 , covers 53,483 square kilometers. Approximately 64% of the land is forested, and 93% is mountainous terrain, bordered by Himachal Pradesh, Uttar Pradesh, China, and Nepal. Serving as the source of major rivers, the state encompasses six significant basins: Yamuna, Alaknanda, Ganga, Kali, Bhagirathi, and Ramganga. Data analysis utilized Shuttle Radar Topographic Mission information obtained from Earth Explorer ( https://earthexplorer.usgs.gov ) via Arc GIS Version 10.5, as shown in Fig.  1 .

( a ) Showing Uttarakhand North western Himalayan state of India ( b ) Nainital district of Uttarakhand with Digital elevation model.

Climate characteristics

The climate of study area exhibits notable variations, ranging from humid subtropical conditions in the Terai region to tundra-like environments in the Greater Himalaya. Substantial transformations occur across the landscape, with high altitudes housing glaciers and lower elevations supporting subtropical forests. Annual precipitation contributes nourishing snowfall to the Himalaya, particularly above 3000 meters 54 . Temperature variations are influenced by elevation, geographical position, slope, and topographical factors. In March and April, southern areas experience average maximum temperature between 34 °C and 38 °C, with average minimum temperatures ranging from 20 °C to 24 °C. Temperatures peak in May and June, reaching up to 42 °C in the lowlands and around 30 °C at elevations exceeding two kilometers. A decline in temperatures begins in late September, reaching their lowest points in January and early February, with January being the coldest month. Southern regions and river valleys witness an average maximum temperature of approximately 20 °C and an average minimum temperature of about 6 °C, while elevation of 2 km above sea level range from 10 °C to 12 °C 55 .

Materials and methodology

Radar is used to collect the rainfall observation remotely. A rain gauge is a conventional method located on the ground for recording rainfall depth in millimeters. Radar systems and rain gauges are standard equipment for tracking significant rainfall events. If there is a widespread, uniform network of rain gauges, it is possible to monitor rainfall accurately unfortunately, there is no such system in Nainital, Uttarakhand, or other parts of India. With the diverse topography of Nainital, Uttrakhand, it is challenging to observe accuracy for extreme rainfall events using radar and rain gauge stations. Satellite observation is the only tool available for monitoring these events. The extreme rainfall event over Nainital, Uttarakhand, was tracked in this study using hourly measurements of rainfall from Meteosat-8 geostationary satellite data. Hourly rainfall measurement was estimated at five kilometres by integrating observation from the Meteosat-8 satellite with space-borne precipitation Radar (PR) from the tropical rainfall measuring mission (TRMM). To estimate rainfall using Meteosat-8 IR and water vapour (WV) channels at 5 km resolution, we have employed the rain index-based technique created by Mishra, 2012 48 . The techniques use TRMM (Tropical rainfall Measuring Mission), space-borne precipitation radar (PR), and Meteosat-8 multispectral satellite data to create the rain analysis. The technique uses Infrared and water vapour observation from Meteosat-8 on 17, 18 and 19 October 2021 to estimate the amount of rainfall over the Nainital, Uttarakhand. By using the infrared (IR) and water vapour (WV) channel observations from Meteosat-8, a new rain index (RI) was computed. The procedure for calculating the rain index is as follows. Non-rainy clouds are filtered out using spatial and temporal gradient approach and brightness temperature from thermal Infrared (TIR) and WV are collocated against rainfall from precipitation radar (PR) to derive non-rainy thresholds of brightness temperature from TIR and WV channels. Now TIR and WV rain coefficient is computed by dividing the brightness temperature from TIR and WV channels with non-rainy thresholds. The TIR ad WV, rain coefficient product, is defined as the rain index (RI). RI is collocated against rainfall from PR to develop a relationship between rainfall and RI using large data sets of heavy rainfall events during the monsoon season of multiple years. The following equation is developed between rain rate (RR) and RI:

Finally, the rainfall rate (RR) is calculated using Eq. ( 1 ). For the Indian subcontinent, a, b, and c are calculated as a = 8.4969, b = 2.7362, and c = 4.27. Using RI generated from Meteosat-8 measurements, this model may be used to estimate hourly rainfall.

The current equation (I) was verified using observations from a strong network of ground-based rain gauges. Hourly rain gauge readings over India during the south-west monsoon season were observed to have a correlation coefficient of 0.70, a bias of 1.37 mm/h, a root mean square error of 3.98 mm/h, a chance of detection of 0.87, a false alarm ratio of 0.13, and a skill score of 0.22 48 . The method used by Mishra 48 outperformed other methods for examining the diurnal aspects of heavy rain over India compared to currently available worldwide rainfall statistics. If both satellite spectral responses to the channels used to produce the rain signatures are similar, the equation developed to estimate rainfall using the rain signature from one satellite can also be used to estimate rainfall using the rain signature from another satellite.

Within the framework of this investigation, Meteosat-8 Second Generation (MSG) measurements were harnessed to scrutinize rainfall characteristics with a heightened focus on fine geographical and temporal scales. Employing the mentioned technique facilitated the calculation of spatial rainfall distribution, as well as the meticulous quantification of hourly and daily rainfall. Subsequently, a comprehensive analysis of cumulative rainfall was conducted, unraveling nuanced patterns and trends within the meteorological data. Following an in-depth examination of intense rainfall episodes, the atmospheric datasets, incorporating cloud optical thickness, total precipitable water vapor, and aerosol optical depth, were procured from Modern-Era Retrospective Analysis for Research and Applications, the National Centers for Environmental Prediction (NCEP), and the National Centre for Atmospheric Research (NCAR). These datasets underwent meticulous scrutiny to unravel the intricate interconnections between atmospheric parameters and heavy rainfall, specifically flash flooding, across the study area. The central objective was to decipher the meteorological conditions catalyzing the genesis of a low-pressure system, subsequently triggering heightened convective activities. To comprehend the dynamics of aerosols within the study domain, trajectory analysis through HYSPLIT was implemented, elucidating trajectories and dispersion patterns of aerosols for comprehensive insights. To comprehensively comprehend episodes of heavy rainfall in the Nainital region of Uttarakhand, particularly during the flash flooding events of October 2021, this study systematically delves into pre-flood parameters. The investigation focuses on Nainital and systematically analyzes time series rainfall data (Modern-Era Retrospective Analysis for Research and Applications) spanning from 2000 to 2021. Monthly rainfall for each year and the long-term mean (accumulated rainfall) were meticulously calculated. Robust statistical tests applied to the time series data unveiled trends, indicating a non-significant increase overall, except for a notable decrease in July. The study further integrates Shuttle Radar Topography Mission (SRTM) topographic data and the total number of cloud burst events ( https://dehradun.nic.in/ ) to elucidate the role of elevation in cloud burst occurrences. Exploring the relationship between elevation, annual rainfall, and maximum temperature, the research establishes critical links between heavy rainfall episodes, flash flooding, and associated loss of lives from 2010 to 2022. The study strategically correlates these aspects with time-series data, presenting instances of heavy rainfall and rapid-onset flooding. Utilizing Meteosat-8 data and remote sensing, our research pioneers dynamic flash flood analysis, shedding light on the pivotal roles played by atmospheric and geographic parameters. The time series precipitation data, spanning from 2001 to 2021, underwent rigorous trend analysis employing statistical methodologies, including Autocorrelation function (ACF), Mann–Kendall (MK) test, Modified Mann–Kendall test, and Sen's slope (q) estimator. These analyses were conducted to elucidate and characterize the prevailing trends within the rainfall dataset over the specified temporal interval.

Autocorrelation function (ACF)

Autocorrelation or serial dependency is one of the severe drawbacks for analyzing and detecting trends of time series data. The existence of autocorrelation in the time series data may affect MK test statistic variance (S) 56 , 57 . Hence, the ACF at lag-1 was calculated using the following equation.

where, r k denotes the ACF (autocorrelation function) at lag k, x t and x p is the utilized rainfall data, x ¯ denotes the mean of utilized data x p , N signify the total length of the time-series ( x p ) , k refers to the maximum lag.

Mann–Kendall (MK) test

In hydroclimatic investigations, the MK test is extensively employed for evaluating trends 58 – 60 . The-MK test 61 , 62 was conferred by the World-Meteorological-Organization (WMO), which has a number of benefits 63 . The following equations can be used to construct MK test-statistic

In Eq. ( 5 ), n denotes the size of the sample, whereas x p and- x q denote consecutive data within a series.

The variance of S is assessed in the following way

whereas t p and q denotes the number of ties for the p th value. Equation ( 9 ) shows how to calculate Z statistic, the standardized-test for the MK test-(Z)

The trend's direction is indicated by the letter Z. A negative Z value specifies a diminishing trend and vice versa. The null hypothesis of no trend will be rejected when the absolute value of Z would be greater than 2.576 and 1.960 at 1% and 5% significant level.

Modified Mann–Kendall test

Hamed and Rao (1998) 64 introduced the modified MK test for auto-correlated data. In the case of auto-correlated data, variance (s) is underestimated 65 ; hence, the following correction factor n n e ∗ is proposed to deal with serially dependency data.

where n is the total number of observations and ρ e f denotes the autocorrelation function of the time series, and it is estimated using the following equation

Sen's slope (q) estimator

Sen 66 proposed the non-parametric technique to obtain the quantity of trends in the data series. The Sen’s slope estimator can calculate in the time series from N pairs of data using this formula

where Q i refers to the Sen’s slope estimator, x n and x m are scores of times n and m , respectively.

Results and discussion

The Himalaya, renowned for their massive size and elevated altitude, possess distinctive geological characteristics that render them vulnerable to sudden and intense floods 67 . These rapid floods are the outcome of a combination of natural and human factors, including geological movements, glacial lakes, steep topography, deforestation, alterations in land usage, and the monsoon season 68 . In the Himalayan region, the primary trigger for these abrupt floods is often linked to instances of cloud bursts accompanied by heavy rainfall episodes 69 . This study aims to provide insight into historical and recent instances of significant rainfall that have resulted in flash floods, while also examining the relationship between these events with atmospheric and other relevant factors. The study also elaborates on the discussion on flash flooding on the 17th, 18 and 19 October 2021. In Fig.  2 we have illustrated the elevation and cloud burst events that occurred between 2020 and 2021 across different districts in Uttarakhand, Himalaya. The elevation map (Fig.  2 ), was generated by Arc GIS 10.5. Using cloud burst data from ( https://dehradun.nic.in/ ). After statistical analyses, the same data was imported to Arc GIS 10.5 and was shown in the form of Fig.  2 . The figure underscores that the northern areas, located within the central portion of Uttarakhand, witnessed a higher frequency of cloud bursts compared to the southern areas. The observed divergence, attributed to steeper slopes in the northern region as opposed to the southern region, is further complemented by an intriguing revelation in our study 70 . Specifically, we noted significantly fewer cloud burst events in the areas of both lower and sharply higher elevations during the period of 2020–2021, particularly when compared with the occurrences at medium elevations from (1000 to 2500)m illustrated in Fig.  2 . Thus, emphasizing a noteworthy and substantiated relationship between cloud bursts and elevation 70 .

Location map of cloudbursts hit area from 2020 to 2021 over Uttrakhand.

Within the specified timeframe, a total of 30 significant cloudburst incidents were documented during 2020–2021, with 17 of these incidents transpiring in 2021. Among the districts, Uttarkashi recorded the highest number of cloudburst occurrences (07), trailed by Chamoli with 05 incidents, while Dehradun and Pithoragarh each registered 04 instances. Rudraprayag accounted for 03 incidents, whereas Tehri, Almora, and Bageshwar each reported 01 cloudburst occurrence, according to reports from the Dehradun District Administration and the India Meteorological Department in 2021.

Due to high topography, the area has faced many flash flood events in history. Figure  3 presents a graphical representation of the total monthly rainfall data for the Nainital district in Uttarakhand from 2000 to 2021. The graph reveals the amount of rainfall received each month throughout this period. A noteworthy observation from the graph is that most of the years between 2000 and 2021 experienced substantial rainfall, with the majority surpassing 300 mm. However, 2010 is an exceptional case of rainfall in the Nainital area. The region received an astounding 500 mm monthly rainfall during this particular year. This extraordinary amount of rainfall was unprecedented and broke the records of the last few decades. Such a significant monthly rainfall level had not been observed in the region for quite some time. The spike in rainfall during 2010 might have considerably impacted the local environment, water bodies, and overall hydrological conditions in the area. Given the intensity of the rainfall, It could have caused flooding, landslides, and other related hazards. The data presented in Fig.  3 is crucial for understanding the long-term trends and patterns of rainfall in Nainital over the past two decades. In Fig.  3 , another intriguing aspect emerges, shedding light on the fact that the South-west monsoon exhibits its peak rainfall during the months of June, July, August, and September across the study area.( https://mausam.imd.gov.in/Forecast/mcmarq/mcmarq_data/SW_MONS OON_2022_UK.pdf).The region could be subject to recurring heavy rainfall episodes, potentially resulting in flash flooding over specific temporal intervals.

Time series monthly rainfall of study area. J(January),F(February),M(March),A(April),M(May),Ju(June)Jl(July),Ag(August), S(September),Oc(October), N(November), D(December).

Figure  4 offers a visual representation of the long-term average of monthly recorded rainfall data in the study area from 2000 to 2021 to gain insight into the average rainfall during the same timeframe. The graph illustrates a significant rise in the average long-term rainfall within the study area. This increase is particularly notable during the months spanning from June to September. Notably, the figure underscores that during the years 2000 to 2021, the months of July and August in the area witnessed multiple heavy rainfall episodes due to monsoon. For these two months, the long-term average surpasses the 300 mm mark. In our results and discussion, we unravel the ramifications of persistent and substantial rainfall throughout these crucial months. The enduring deluge sets in motion a series of impactful consequences, ranging from escalated surface runoff and heightened river discharge to the looming specter of rapid flooding and landslides. This intricate web of effects intricately influences the stability of the soil, the vitality of vegetation, and the delicate balance within local ecosystems 71 . The findings highlighted in Fig. (3 and 4) underscore the critical significance of examining monthly rainfall data to comprehend the relationship with average monthly rainfall trends from (2000–2021) in the Himalayan region. The figure specifically draws attention to the months characterized by substantial rainfall, which may have result in disasters such as flash flooding and landslides. So we have concluded the study area may have received flash flooding by heavy rainfall during June to September (2000–2021).The daily rainfall data from 2001 to 2021 was allowed for non parametric trend analyses using Mann–Kendall test, Sen’s slope analysis. Modified Mann–Kendall and autocorrelation function for trend analysis.

Accumulated rainfall (Long-term mean) over the Study area.

Our analysis delved into daily rainfall data, downloaded from (ww.nasa.giovanni.com). We aimed to discern trends in key parameters, including monthly rainfall during the monsoon season (June to September), monsoon season data, annual rainfall, heavy rainfall events (> 50 mm/Day), and the number of wet days (> 2.5 mm/Day). Table ​ Table1 1 provides a comprehensive analysis of rainfall trends and extreme rainfall events from 2000 to 2021. In June, a negative autocorrelation was observed, and the findings are statistically significant at a 95% confidence level, so we considered modified MK test instead of original MK test. Employing the non-parametric Mann–Kendall test (MK/mMK) for trend analysis, our findings revealed a general non-significant increasing trend, with the exception of July, which exhibited a non-significant decreasing trend. Noteworthy was the significant increase in the number of wet days at a 0.05% significance level. Sen’s slope analysis further emphasized an annual increase in rainfall at a rate of 4.558 mm. These results provide valuable insights into the evolving rainfall patterns in the studied region, with implications for understanding climate variations.

Trend analysis of rainfall and extreme rainfall 2000–2021.

*Significant at 99% Confidence Interval.

Topographic influence on rainfall and temperature over the study area

Exploring the realm of abundant rainfall at lofty Himalayan elevations delves into the captivating interplay between topography and the dynamic shifts in atmospheric parameters. Our investigation ventures beyond the surface, intricately analyzing the elevations across diverse districts within our study area. Figure  5 serves as a visual gateway, unraveling the fascinating discourse on how these elevational nuances weave a compelling narrative of change, orchestrating the dance between rainfall patterns and temperature shifts across our meticulously examined landscape. Using Fig.  5 , we can correlate the significant relationship between the amount of rainfall and the topography over the Himalayan region of Uttarakhand. The figure distinctly delineates various districts of Uttarakhand, such as Bageshwar, Chamoli, Nainital, Pithoragarh, Rudraprayag, and Tehri Garhwal, positioned at elevations surpassing 7000 m. The presented data establishes a conspicuous correlation between the received rainfall and the elevated nature of these districts, showcasing those areas above 7000 m experience substantial annual rainfall exceeding 1500 mm. This correlation underscores the notable influence of elevation on the precipitation patterns in the Himalayan region. Higher elevations tend to attract more moisture from the atmosphere, leading to increased rainfall 72 .

Topographic influence on the atmospheric parameter (Temperature and rainfall).

Figure  5 , in conjunction with the citation of Rafiq et al. 2016 73 , emphasizes the significant connection between mean maximum temperature and elevation within the Himalayan region. The figure illustrates that as elevation increases, there is a corresponding decline in mean maximum temperature. This well-known phenomenon is called the "lapse rate," which describes the temperature decrease with rising altitude. Areas above 7000 m experience notably lower temperatures than those at lower elevations. The lapse rate is a fundamental climatic characteristic particularly relevant in mountainous terrains like the Himalaya. As air ascends along the slopes, it cools down due to decreasing atmospheric pressure, forming clouds through condensation. These clouds subsequently contribute to rainfall, as discussed in the study by Wang Keyi et al. 72 . Higher elevations experience a more pronounced temperature decrease, resulting in elevated rainfall levels.

The steep slopes in the Himalayan region significantly correlate with the number of casualties resulting from cloud bursts, landslides, and flash floods caused by heavy rainfall events. The presence of steep gradients exacerbates the impact of sudden and intense rainfall, leading to flash floods and landslides. Topography is crucial in disasters, particularly flash flooding and landslides, commonly observed in the Himalayan region 2 . These natural disasters have resulted in substantial loss of life and livelihood, as depicted in Fig.  6 .  Over 300 casualties were reported due to landslides, flash flooding, and cloud bursts in Uttarakhand during 2021. From 2010 and 2013, the loss was restricted to nearly 230 causalities each year. The Himalayan steep gradients are especially vulnerable to the effects of rainfall and climate change 74 .

Number of human lives lost during heavy rainfall episodes in Uttrakhand.

Moreover, these mountainous regions' ecological and socioeconomic systems are becoming increasingly vulnerable due to the rising human population 2 . These disasters cause severe damage to infrastructure, properties, human lives, and the environment. Furthermore, they can exacerbate other hardships, including the spread of diseases, financial instability, environmental degradation, and social conflicts 74 .

In summary, the steep slopes in the Himalayan region play a critical role in the occurrence and severity of disasters such as flash floods and landslides. The susceptibility of these areas to heavy rainfall and climate impacts poses significant challenges for ecological and socioeconomic systems, particularly with the increasing human population. The aftermath of these disasters is far-reaching and extends beyond the immediate loss of life and property, affecting various aspects of human life and the environment in the region.

Flash flood event during October 2021

As delineated in Fig. ​ Fig.7, 7 , our investigation reveals a distinctive pattern in precipitation dynamics. Traditionally, the region encounters heightened rainfall exclusively from June to September, aligning with the monsoon season. Flash flooding, consequently, primarily manifests during this period. However, the anomalous occurrence in October 2021 is unprecedented in our dataset. For the first time, our analysis, depicted in Fig.  7 , captures the manifestation of intense rainfall episodes leading to flash flooding in the Nainital region, Uttarakhand. As this was the rare case the study area has received heavy raifnall during month of october 2021. This may be due to western distribuance that area very rarely is receiving. The infrequency of such events in the area may be attributed to the rarity of western disturbances impacting the region. Utilizing the technique developed by Mishra 48 , we conducted the study to map daily monthly and spatial distribution of rainfall amount using Meteosat-8 data. The study employs real-time monitoring to track and analyze flash flooding, shedding light on the atmospheric parameters that contributed to the occurrence of this unique episode.

Time series heavy rainfall episodes over the Study area.

During October 2021, the region of Nainital, Uttarakhand experienced a series of rainfall events. From 12 to 15 September 2021, the area witnessed the development of low-pressure systems from the Bay of Bengal, as documented in the IMD Report 2021. This convergence of low-pressure systems led to several episodes of heavy rainfall over the Himalayan region 74 . Unfortunately, the consequences of these multiple rainfall episodes were severe, causing flash flooding and triggering landslides in various parts of the Indian Himalaya. Over the past few decades, there has been a noticeable upward trend in flash flooding incidents, particularly in the Himalayan region, which can be attributed to the effects of climate change 75 . As global temperatures rise and weather patterns become more erratic, the delicate balance of the Himalayan ecosystem is being disrupted, leading to intensified rainfall events and a higher risk of natural disasters like flash floods and landslides. These alarming changes underscore the urgent need for climate action and measures to address the impacts of climate change on vulnerable regions like the Himalaya. In October 2021, Nainital, Uttarakhand experienced an unusual and devastating flood event, an occurrence that is typically rare during this particular month. The torrential floodwaters swept away numerous homes and disrupted transportation networks, leaving the region in turmoil. In response to this calamity, various defence groups, such as the army and national defense forces, were promptly deployed to the Himalayan state to conduct rescue operations for residents and tourists. The impact of the flood was further exacerbated by landslides, which severed many districts from the rest of the region, as roads were blocked by mud and debris. The region's vulnerability to such natural disasters can be traced back to historical records, as it has been experiencing substantial rainfall since as early as 1857 76 . During 17th, 18th, and 19th of October 2021 a series of heavy rainfall episodes in Nainital, Uttarakhand, leading to flash flooding and landslides. The dire consequences resulted in widespread destruction of both lives and livelihoods 2 . Figure  7 highlights the visual representation of rainfall distribution over three days. The illustration provides valuable insights into the amount and pattern of rainfall that occurred during this critical period. Notably, the data reveals a remarkable occurrence on the 18th and 19th of October, where the study area experienced an abrupt 270 mm of rainfall. This substantial rainfall in just two days is an alarming and unprecedented event, signifying the intensity and severity of the weather system that hit the region. Moreover, it is essential to note that the 270 mm rainfall figure is not solely confined to those two days but is the cumulative result of heavy rainfall from multiple rainy spells that persisted during the specified period. The confluence of these rain events led to an overwhelming deluge, which became a primary driver of the extreme flooding that engulfed Nainital, Uttarakhand.

The analysis of near real-time monitoring of flash flooding in the area involved examining pre-flood atmospheric data related to aerosol optical depth, cloud optical thickness and total perceptible water vapour over the study area, as depicted in Fig.  8 b,c,d. The study revealed a significant correlation between the pre-flood atmospheric data and the occurrence of extreme and multiple rainfall episodes in the region. This indicates that cloud formation and the presence of moisture are closely linked to the presence of aerosol particles 77 . The analysis of aerosol data in the study area revealed a significant presence of aerosol content in the atmosphere before the flood. This observation was particularly evident from the data recorded between the 5th and 8th of October 2021, as depicted in Fig.  8 . The aerosol optical depth during this period was measured to be around 0.8, a noteworthy value for its potential impact in inducing heavy rainfall and flash flooding 78 , 79 . Aerosols are tiny particles suspended in the air, which can have important implications for weather and climate patterns 80 .  High aerosol optical depth, as indicated by the measurement of 0.8, suggests a relatively dense concentration of aerosol particles in the atmosphere during the specified timeframe. Such high aerosol levels can act as cloud condensation nuclei, providing necessary sites for water vapour to condense and form cloud droplets. This phenomenon is crucial for cloud formation and rainfall processes 81 .  The significance of aerosols in cloud formation lies in their ability to serve as nuclei for the aggregation of water vapour, leading to the development of clouds. This thick cloud cover resulted in considerable precipitable water vapour from the 17th to 19th of October, as shown in Fig.  8 82 , 83 . These atmospheric parameters resulted in favorable conditions for extreme with multiple rainfall episodes over the study area from 17 to 19th October 2021,finally, the extreme rainfall episodes attributed to flash flooding over the Nainital, Uttarakhand.

( a ) Cumulative rainfall over the Nanital Utrankhand, ( b ) Aerosol optical depth over the Nanital Utrankhand, ( c ) Cloud optical thickness over the Nanital Utrankhand, ( d ) Total Perceptible water Vapor over the Nanital Utrankhand.

When moisture condenses around aerosol particles, it contributes to the formation of larger cloud droplets. These larger droplets can result in more intense rainfall events, potentially leading to flash flooding under certain conditions 82 , 83 . Furthermore, the HYSPLIT trajectory analysis revealed a profound influence of air masses originating or passing through western regions on the Himalayan radiation budget. This suggests that atmospheric dynamics from these areas significantly impact the weather patterns and climate in the Himalayan region. To gain deeper insights into the role of aerosols in the Himalayan radiation budget, the study also examined the Atmospheric Radiative Forcing (ARF) 14 . In the investigation of aerosol data, a backward trajectory analysis was conducted depicted in Fig.  9 , focusing on the 17th and 18th of October 2021. The analysis aimed to trace the movement and direction of aerosols in the atmosphere 48 h before reaching the target area encircled in Fig.  9 . The findings of figure demonstrated journey of aerosol during these days, shedding light on their movement and behavior in the study area. Specifically, on the 17th of October, the source of aerosols was observed at an altitude of 3500 m above Mean Sea Level (MSL). The tracked trajectory of aerosols reveals a gradual descent from an initial altitude of 3500 m above Mean Sea Level (MSL), ultimately reaching the research target at 1096 m MSL. This horizontal movement of aerosols suggests a potential influential role in the occurrence of heavy rainfall that result flash flooding over the study area by providing the favorable atmospheric conditions.

Backward trajectory of Aerosol during 17th, 18th and 19th October 2021 over the study area source encircled.

The comprehensive analysis conducted in this study has significantly advanced our understanding of the intricate interactions between various atmospheric parameters, aerosols, and rainfall patterns, all of which collectively contribute to heavy with multiple rainfall episodes that resulted flash flooding event in the Nainital region of Uttarakhand. The severity of such flash floods is starkly evident from the tragic loss of fifty lives and the extensive damage to property and infrastructure.

A key highlight of this study is the application of remote sensing data, including total aerosol optical depth, cloud cover thickness, total precipitable water vapour, and rainfall product (Meteosat-8), for real-time monitoring of flash floods. The use of cutting-edge satellite technology and geospatial data has proven to be pivotal in closely monitoring and tracking flash floods, enabling timely and efficient responses to mitigate the impact of these disasters. The findings of this research underscore the vital importance of leveraging advanced technology and scientific research to address the challenges posed by flash flooding in the Himalayan region. To effectively combat these challenges, a comprehensive and multi-faceted approach is imperative. This may encompass implementing measures to counteract the impact of climate change on weather patterns, advocating for sustainable land use practices to reduce vulnerability, and bolstering the resilience of critical infrastructure to withstand the impacts of extreme weather events like flash floods.

Furthermore, the study presents a unique occurrence in the Nainital region of Uttarakhand, Himalaya, wherein heavy rainfall, marked by multiple episodes, led to flash flooding during October 2021, an unusual event when compared to the time series precipitation analyzed in the study. The investigation emphasizes the significant role of elevation in influencing rainfall and temperature variations in the region. The study emphasizes the significance of continuous scientific research and monitoring efforts to gain invaluable insights into the underlying patterns and drivers of flash flooding in the Himalaya. Armed with this knowledge, authorities can formulate robust strategies and policies to minimize the impact of future flash floods and safeguard the lives and livelihoods of the communities residing in the region. This study reaffirms the crucial role that satellite data and geospatial technology play in effective disaster management. It underscores the urgency of adopting proactive measures to address the mounting risks of flash floods in vulnerable regions like Nainital, Uttarakhand. By synergizing scientific research, advanced monitoring techniques, and community engagement, authorities can work towards building a more resilient future, better equipped to respond to and mitigate the repercussions of flash flooding events.

With their immense size and unique geological features, the Himalaya are prone to flash flooding incidents that pose significant risks to human life and infrastructure. Natural factors, such as tectonic activities and glacial lakes, and human-induced changes, including deforestation and land use alterations, influence these flash floods. In the Nainital region of Uttarakhand, the primary cause of flash floods is often attributed to cloud bursts accompanied by heavy rainfall episodes. The study highlights the crucial role of rainfall product and remote sensing data including total aerosol optical depth, cloud cover thickness and total precipitable water vapour, in real-time short-lived flash flood monitoring. The study emphasizes the significant role of elevation in influencing rainfall and temperature variations in the region. The application of satellite technology and geospatial data has proven to be instrumental in promptly tracking and responding to flash flood events. A comprehensive approach is necessary to address the challenges of flash flooding in the Himalaya. This may involve implementing measures to mitigate the impact of climate change, promoting sustainable land use practices, and enhancing infrastructure resilience. The study highlights a significant shift in precipitation patterns of Nainital, with usual heightened rainfall and flash floods. The rarity of such events in the region may be linked to infrequent western disturbances.

The research contributes valuable historical data and insights into the patterns of heavy rainfall and flash floods in the region. It underscores the alteration in precipitation patterns attributed to variations in atmospheric parameters over the study area. The findings demonstrate continuous monitoring and scientific research are critical for developing effective strategies to mitigate the impact of flash floods and safeguard communities in vulnerable regions like Nainital Uttarakhand. Overall, this study emphasizes the urgent need for climate action and proactive measures to address the rising risks of flash floods. By integrating advanced technology, scientific research, and community engagement, authorities can work towards building a more resilient future and better preparedness to tackle extreme weather events ( Supplementary Information ).

Supplementary Information

Author contributions.

Conceptualization, validation, writing review, editing and supervision was carried out by K.N. and A.K.M. Methodology, software, formal analysis, writing original draft preparation was carried out by M.S.M. and J.D. All authors have read and agreed to the published version of the manuscript.

The work was funded by HRDG CSIR through Grant Number 23(0034)/19/EMR-II. CSIR.

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Competing interests.

The authors declare no competing interests.

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Understanding flash flooding in the Himalayan Region: a case study

Affiliations.

• 1 Centre for Remote Sensing and Geoinformatics, Sathyabama Institute of Science and Technology, Chennai, India.
• 2 Office of Director General of Meteorology, India Meteorological Department, Ministry of Earth Science, SATMET Division, Mausam Bhavan, New Delhi, 110003, India.
• 3 Centre for Remote Sensing and Geoinformatics, Sathyabama Institute of Science and Technology, Chennai, India. [email protected].
• 4 Department of Geography, Rampurhat College, PO- Rampurhat, Birbhum, 731224, India.
• PMID: 38528024
• PMCID: PMC10963777
• DOI: 10.1038/s41598-024-53535-w

The Himalayan region, characterized by its substantial topographical scale and elevation, exhibits vulnerability to flash floods and landslides induced by natural and anthropogenic influences. The study focuses on the Himalayan region, emphasizing the pivotal role of geographical and atmospheric parameters in flash flood occurrences. Specifically, the investigation delves into the intricate interactions between atmospheric and surface parameters to elucidate their collective contribution to flash flooding within the Nainital region of Uttarakhand in the Himalayan terrain. Pre-flood parameters, including total aerosol optical depth, cloud cover thickness, and total precipitable water vapor, were systematically analyzed, revealing a noteworthy correlation with flash flooding event transpiring on October 17th, 18th, and 19th, 2021. Which resulted in a huge loss of life and property in the study area. Contrasting the October 2021 heavy rainfall with the time series data (2000-2021), the historical pattern indicates flash flooding predominantly during June to September. The rare occurrence of October flash flooding suggests a potential shift in the area's precipitation pattern, possibly influenced by climate change. Robust statistical analyses, specifically employing non-parametric tests including the Autocorrelation function (ACF), Mann-Kendall (MK) test, Modified Mann-Kendall, and Sen's slope (q) estimator, were applied to discern extreme precipitation characteristics from 2000 to 201. The findings revealed a general non-significant increasing trend, except for July, which exhibited a non-significant decreasing trend. Moreover, the results elucidate the application of Meteosat-8 data and remote sensing applications to analyze flash flood dynamics. Furthermore, the research extensively explores the substantial roles played by pre and post-atmospheric parameters with geographic parameters in heavy rainfall events that resulted flash flooding, presenting a comprehensive discussion. The findings describe the role of real time remote sensing and satellite and underscore the need for comprehensive approaches to tackle flash flooding, including mitigation. The study also highlights the significance of monitoring weather patterns and rainfall trends to improve disaster preparedness and minimize the impact of flash floods in the Himalayan region.

© 2024. The Author(s).

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Conflict of interest statement

The authors declare no competing interests.

( a ) Showing Uttarakhand…

( a ) Showing Uttarakhand North western Himalayan state of India ( b…

Location map of cloudbursts hit…

Location map of cloudbursts hit area from 2020 to 2021 over Uttrakhand.

Time series monthly rainfall of…

Time series monthly rainfall of study area. J(January),F(February),M(March),A(April),M(May),Ju(June)Jl(July),Ag(August), S(September),Oc(October), N(November), D(December).

Accumulated rainfall (Long-term mean) over…

Accumulated rainfall (Long-term mean) over the Study area.

Topographic influence on the atmospheric…

Topographic influence on the atmospheric parameter (Temperature and rainfall).

Number of human lives lost…

Number of human lives lost during heavy rainfall episodes in Uttrakhand.

Time series heavy rainfall episodes…

Time series heavy rainfall episodes over the Study area.

( a ) Cumulative rainfall…

( a ) Cumulative rainfall over the Nanital Utrankhand, ( b ) Aerosol…

Backward trajectory of Aerosol during…

Backward trajectory of Aerosol during 17th, 18th and 19th October 2021 over the…

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Flash Flood Risk Assessment and Driving Factors: A Case Study of the Yantanxi River Basin, Southeastern China

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• Published: 13 April 2022
• Volume 13 , pages 291–304, ( 2022 )

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• Liutong Chen 1 , 2 ,
• Zhengtao Yan 1 , 2 ,
• Qian Li 1 , 2 &
• Yingjun Xu 1 , 2 , 3  

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In the context of climate change, the impact of extreme precipitation and its chain effects has intensified in the southeastern coastal region of China, posing a serious threat to the socioeconomic development in the region. This study took tropical cyclones–extreme precipitation–flash floods as an example to carry out a risk assessment of flash floods under climate change in the Yantanxi River Basin, southeastern China. To obtain the flash flood inundation characteristics through hydrologic–hydrodynamic modeling, the study combined representative concentration pathway (RCP) and shared socioeconomic pathway (SSP) scenarios to examine the change of flash flood risk and used the geographical detector to explore the driving factors behind the change. The results show that flash flood risk in the Yantanxi River Basin will significantly increase, and that socioeconomic factors and precipitation are the main driving forces. Under the RCP4.5-SSP2 and RCP8.5-SSP5 scenarios, the risk of flash floods is expected to increase by 88.79% and 95.57%, respectively. The main drivers in the case study area are GDP density ( q = 0.85), process rainfall ( q = 0.74), asset density ( q = 0.68), and population density ( q = 0.67). The study highlights the influence of socioeconomic factors on the change of flash flood disaster risk in small river basins. Our findings also provide a reference for regional planning and construction of flood control facilities in flash flood-prone areas, which may help to reduce the risk of flash floods.

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1 Introduction

Flood disasters induced by extreme precipitation events have become a major challenge to regional security and development. The Intergovernmental Panel on Climate Change (IPCC) released a special report about managing the risks of extreme events and disasters in 2012, which indicates that the evolution of extreme disaster events such as floods has become an important issue to be addressed in climate change impact and adaptation research (Lavell et al. 2012 ; Fang et al. 2014 ). Climate change has increased the frequency and intensity of extreme precipitation events (Su et al. 2015 ; Luo et al. 2016 ). Hourly precipitation data from 1215 stations in China show that the precipitation intensity and maximum hourly precipitation increased by 0.7–1.1% and 0.9–2.8% on average per decade, respectively, during 1961−2012, mainly in central China and southeastern coastal areas (Jian et al. 2020 ). Extreme precipitation events increase the risk of flood disasters (Su et al. 2015 ; Luo et al. 2016 ). With the rapid socioeconomic development in recent decades, about USD 70.6 billion in economic damages and 4354 casualties were caused by flooding in China’s coastal region during 1989−2014 (Fang et al. 2020 ). Therefore, understanding the spatial and temporal evolution characteristics of flood disasters and carrying out the risk assessment of flood disasters in coastal areas in the context of climate change is important for the sustainable development of the region.

Floods are classified as riverine floods, coastal floods, and flash floods depending on the area where the flood disaster occurs (Griffiths et al. 2019 ). Flash floods have a major impact in China (Zhang et al. 2006 ; Liu et al. 2018 ). They have been distributed in 2058 counties, with a distribution area of 4.87 million km 2 , and flash flood disasters have affected a population of 570 million people before 2016, according to the State Flood Control and Drought Relief Office of China (Cui and Zou 2016 ).

The main methods of flood risk assessment include evaluation index and numerical model simulation. Common numerical simulation models include SWAT, CaMa-Flood, and FLO-2D (Hurkmans et al. 2010 ). Hirabayashi et al. ( 2013 ) used the multiple General Circulation Models (GCMs) of CMIP5 coupled with the CaMa-Flood model to simulate the global 100a flood inundation characteristics, and they estimated that the population exposed to flood disasters will increase tenfold by the end of the 21st century. In the existing studies, flood risk assessment was mainly conducted for large- or medium-sized basins. One of the main reasons for the lack of flash flood risk assessment in small basins under climate change is the coarse accuracy of climate models in simulating rainfall conditions, which could not consider factors such as tropical cyclones (TCs), topography, and regional circulation that cause extreme rainfall within small areas (Lafon et al. 2013 ).

Flood damage assessment is mostly conducted by considering direct economic damage (Armal et al. 2020 ). Quantitative assessment of flood disaster risk is usually based on a damage model, which combines the characteristics of the inundation depth, inundation extent, flow velocity, or other hazard factors of flood events with socioeconomic elements (Dutta et al. 2003 ; Merz et al. 2010 ). But the existing flash flood risk assessments often focus on the characteristics of the hazard, with less attention paid to the joint effect of climate change and socioeconomic changes on the risk drivers of flash floods (Liu et al. 2018 ).

In this study, we conducted a quantitative assessment of flash flood disaster risks. The study took the tropical cyclones–extreme precipitation–flash floods chain effects as an example, and had two main objectives. The first was to obtain the changes of flash flood risk under climate change, and the second was to explore the driving factors behind the changes of flash flood risk. We selected the Yantanxi River Basin in Yongjia County, Zhejiang Province as the study area, which is frequently affected by flash floods. We first constructed an index system of flash flood risk assessment and used the hydrologic-hydrodynamic model to determine the inundation characteristics with the same probability of flash flood disaster events. Second, we combined the result of the hazard identification with the regional asset value, and assessed the flash flood risk through the regional damage model considering climate change. Third, the geographical detector model was used to explore the driving factors of the flash flood disaster risk changes.

2 Data and Methods

In this section, first, the characteristics of the main geographical elements in the study area are described. Second, we describe the multi-source data used in this study. Finally, the risk assessment method of flash flood is described in detail.

2.1 Study Area

Yongjia County is in Wenzhou City, Zhejiang Province (Fig. 1 ), with a land area of 2677 km 2 . Yongjia is located on the north bank of the lower reaches of the Oujiang River, and four mountainous rivers, including the Nanxi River, are distributed in the region. The area of rivers and lakes is 112.7 km 2 , accounting for 4.2% of the county’s area, and the landforms include mountains, hills, and plains, of which hills and mountains account for 85.6% of the county area. Yongjia is in the subtropical monsoon climate zone with a warm and humid climate. From 1949 to 2000, the county was affected by 60 tropical cyclones, accounting for approximately 35% of the total TCs that affected Zhejiang Province. During this time period, 29 TCs caused flood disasters in Yongjia, and 71% of the flash flood disaster events were caused by tropical cyclone-induced extreme precipitation (Dai 2006 ; Liao 2009 ). Yongjia County has a history of more than 1,800 years, with rich natural and ecological resources. The county received 15.57 million tourists and achieved tourism revenues of CNY 17.97 billion Footnote 1 in 2019, which accounted for 40% of the county-level revenues (Yongjia County Bureau of Statistics 2020 ). The regional tourism resources and tourists are exposed to the risk of flash floods (Liao 2011 ).

Geographical location of the study area in southeastern China (the red boundary delineates the main study area)

As a major upstream tributary of the Nanxi River, the Yantanxi River is originated in the northwest of the Dashijian Mountain (elevation of the highest peak is 1240 m), with a total length of 131.89 km. In the upstream tributaries of the Nanxi River, the Shizhu hydrological station manages the hydrological information. The Yantanxi River Basin is in the northern part of Yongjia County (120°24′E−120°48′E and 28°22′N−28°36′N). The total watershed area is 687.62 km 2 (Fig. 1 ) and covers four administrative units in Yantan Town with a total population of 20 thousand according to the 2017 census. The area is famous for the dense distribution of ancient villages and the Sihai Mountain Forest Park. In July 2005, Super Typhoon Haitang made landfall in Wenzhou, and the Yantanxi River Basin experienced heavy rainfall from 0:00 on 19 July to 11:00 on 20 July. During the impact period, the flood level reached 8.55 m in Yongjia urban area, and the main roads were washed out, with direct economic damage amounting to CNY 3.48 billion (in 2019 prices), and the tropical cyclone-induced rainfall further triggered flash floods in the northern mountainous areas, including the Yantanxi River Basin (Liao 2009 ). The natural resources and socioeconomic elements of the Yantanxi River Basin are frequently exposed to flash floods, making it necessary to develop a study of the flash flood risk changes within the area.

The data used in this study include meteorological data (the observed rainfall, tropical cyclone track, and climate model data), geographic information data (DEM, soil type, and land use), and socioeconomic data (GDP, population, and asset values).

2.2.1 Meteorological Data

Tropical cyclone track data were retrieved from the Shanghai Typhoon Institute of the China Meteorological Administration (CMA-STI) tropical cyclone best track dataset. Footnote 2 The dataset includes the typhoon time; the typhoon center latitude and longitude, minimum pressure, and maximum wind speed; and the typhoon scale elements (Ying et al. 2014 ).

The observed rainfall data were derived from the hourly precipitation dataset of the ground climate data also provided by the CMA. There are six meteorological stations that contribute the rainfall data in the study area—Jinyun, Yueqing, Linhai, Qingtian, Yongjia, and Xianju. The weight of each meteorological station’s rainfall contribution to the rainfall of the study area is calculated by the ratio of the area between the river basin and the Thiessen polygon (Fig. 1 ). The calculation result shows that Yongjia station has the largest contribution to the regional rainfall. The observed rainfall data from 1971–2019 were selected for each station to ensure the same rainfall time series length.

Climate model data were obtained from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) high-resolution dataset released in 2015. Footnote 3 This dataset downscales the 21 climate models participating in CMIP 5 by bias correction, including precipitation and temperature, with a spatial resolution of 0.25° × 0.25° and a temporal resolution of daily (Chen et al. 2017 ). The two representative concentration pathways (RCPs) were RCP4.5 and RCP8.5. The RCP4.5 scenario assumes a reduction of greenhouse gas emissions with government intervention as a medium concentration emission scenario that is similar to the current climate change scenario (Moss et al. 2010 ; Hurtt et al. 2011 ). The RCP8.5 scenario is without government intervention and with a predominantly fossil fuel energy source, which is a high concentration emission scenario. The use of RCP4.5 and RCP8.5 scenarios for flash flood risk assessment therefore allows us to visualize changes in flash flood risk at current levels of climate change and under extreme changes. The BCC-CSM1.1 data, which exhibit a better rainfall simulation capability in China, were selected from the dataset to define future rainfall scenarios of the study area (Chen 2013 ; Jiang et al. 2015 ). For this research, we retrieved both historical rainfall data (1950–2005) and forecast rainfall data (2052–2100) from the dataset.

2.2.2 Geographic Information Data

These data were mainly used for flash flood inundation simulation. A digital elevation model (DEM) was obtained from the Geospatial Data Cloud constructed by the Chinese Academy of Sciences, Footnote 4 with a spatial resolution of 30 m. The land use data are based on the Landsat 8 interpretation of the land surface characteristics of China in 2018, with a spatial resolution of 30 m, and were derived from the Resource and Environment Science and Data Center of China. Footnote 5 Soil type distribution data were obtained from the Harmonized World Soil Database (HWSD) published by the Food and Agriculture Organization (FAO) and available at the National Cryosphere Desert Data Center of China. Footnote 6

2.2.3 Socioeconomic Data

The socioeconomic data analyzed in this study were derived from the Wenzhou Statistical Yearbook (Wenzhou Municipal Bureau of Statistics 2019 ), including population and GDP data for 2005–2019. Disaster data were mainly acquired from the Meteorological Disaster Yearbook, Zhejiang Volume (Wen et al. 2006 ) and the Records of Water Conservancy in Yongjia (Dai 2006 ), and were used to verify the impact of tropical cyclones in the study area.

The asset value data were retrieved from previous studies. Wu et al. ( 2014 ) estimated the asset values of 344 prefecture-level cities in China with the perpetual inventory method. In addition, the researchers combined geographic information data such as lights, roads, and population density to achieve a spatialized dataset of county-level assets and updated this dataset based on 2019 prices (Wu et al. 2017 ; Wu et al. 2018 ).

The IPCC developed the shared socioeconomic pathways (SSPs) in 2011. To bring the dataset more in line with Chinese socioeconomic characteristics, Nanjing University of Information Science and Technology (NUIST) corrected the datasets based on parametric methods, with a spatial resolution of 0.5° × 0.5°. Footnote 7 The University used 2010 as the base year and data on key factors such as fertility, mortality, migration rate of the current population, capital stock of the economy, labor force participation rate, and total factor productivity in China were accumulated and form the population and GDP dataset before 2100. This dataset provides data support for studies related to climate change risks at regional and river basin scales, mainly in the areas of energy, water resources, and agriculture (Jiang et al. 2018 ; Huang et al. 2019 ). This study used the corrected dataset and combined the RCP and SSP scenarios (RCP4.5-SSP2, RCP8.5-SSP5) to develop the assessment of the flash flood risk changes in the study area.

2.3 Methods

Based on the regional disaster system theory, the flash flood risk refers to the possible damage in regional socioeconomic systems caused by flood disaster events triggered by heavy precipitation that occurs in mountainous and hilly and river valley areas (Zhang et al. 2006 ; Shi 2016 ). The flash flood risk can be expressed as (Asian Disaster Reduction Center 2005 ):

In assessing the flash flood risk, the first step is to analyze the intensity and probability of historical flash flood events to derive the characteristics of flash flood inundation in a case area ( Hazard ). The second step is to obtain the asset value in areas affected by flash floods ( Exposure ). The third step is to combine the survey of the damage status caused by historical flood disaster events and obtain the economic damage rate in the region ( Vulnerability ). Finally, the economic damage under a certain probability of occurrence is determined, and the Risk indicates the variation of economic damage under different scenarios considering the capacity of disaster prevention.

2.3.1 Constructing an Indicator System for Flash Flood Risk Assessment

Considering the characteristics of flash flood hazards, regional exposure, and reference to existing studies (Hu et al. 2018 ; Liu et al. 2018 ; Ye et al. 2019 ; Shi et al. 2020 ), an indicator system of flash flood risk assessment was constructed in this study (Table 1 ). We chose eight indicators—process rainfall (PR), impact force (IF), flow velocity (FV), population density (PD), GDP density (GD), asset density (AD), per capita GDP (PG), and per capita asset value (PA)—to describe the hazard, exposure, and capacity of disaster prevention for the risk assessment of flash flood disasters.

2.3.2 Flash Flood Hazard Analysis

2.3.2.1 regional extreme rainfall characteristics.

Extreme rainfall events often occur along the southeastern coast of China due to the impact of tropical cyclones (TCs), which in turn induce floods in the region (Qiu et al. 2019 ; Fang et al. 2020 ). In this study, TC-induced rainfall is defined as extreme precipitation. Return period (RP) is usually used to predict the probability of the occurrence of extreme hydrometeorological events (Fang et al. 2011 ), thus a RP was defined as the probability of a flash flood disaster event occurring. Based on this definition an analysis of flash flood hazard changes was developed. Existing assessments of the modeling capabilities of climate models indicate that there is a lack of consideration of extreme rainfall characteristics even though the models have been downscaled (Lafon et al. 2013 ; Chen et al. 2017 ). Therefore, it is necessary to extract the extreme rainfall characteristics in the region and take them into consideration in the rainfall model to obtain the flash flood hazard change under future scenarios (Zhang et al. 2019 ).

In the southeastern coastal areas of China, TC-induced rainfall events occur from June to September (Qiu et al. 2019 ), so we considered only the rainfall characteristics during this period of the year. Spatially, the geographic location of the six meteorological stations was adopted as the center, and if a TC moving center is located within 5° (approximately 500 km) from the longitude and latitude coordinates of the station position, the rainfall at the station can be considered a TC effect (Fang et al. 2011 ). Temporally, the rainfall during TC impact periods can be defined as tropical cyclone-induced rainfall (Fang et al. 2011 ). Accordingly, we obtained a set of 153 TCs that affected the Yantanxi River Basin in 1971−2019. In this study, we considered the beginning of a continuous rainfall event as the moment when the hourly rainfall surpasses 4 mm, and the end of the rainfall event occurs when the rainfall remains below 4 mm over the next 6 consecutive hours (Chen et al. 2019 ). We compared the observed rainfall events and the impact TCs, and separated the rainfall into extreme precipitation and regular precipitation. Accordingly, the annual maximum (AM) sequences of observed (1971–2019) extreme rainfall events can be constructed, the Gumbel distribution function was adopted to fit the AM sequences of precipitation to obtain the RPs (Fang et al. 2011 ), and the RPs of precipitation in the historical scenario were obtained.

As the climate model data are available as grid data, rainfall characteristics for the six meteorological stations under the RCP4.5 and RCP8.5 scenarios were obtained by linear interpolation. Thus, the AM sequences of precipitation under climate change scenarios (1950−2100) can be constructed. To include the extreme rainfall characteristics of case area in the climate model, the mean ratio between the rainfall and rainfall intensity of the extreme precipitation events and regular precipitation events at the six meteorological stations was used as the model rainfall correction factor. This correction coefficient was used as the starting point, with step sizes of 0.1 and 20 steps to determine the optimal correction factor. We multiplied the AM sequence of the historical scenario in the climate model (1950−2005) by the optimal correction coefficient to derive the corrected series. Comparing the observed (1971–2019) rainfall and model-corrected (1950−2005) rainfall of same RPs (100a, 500a, 1,000a, and 1,500a), the mean relative error between the two sequences was 0.67%, which occurred within the acceptable error range after considering the extreme characteristics of flood disaster events (Mishra et al. 2018 ). Likewise, this method is appropriate for the future scenarios (2052−2100) correction.

2.3.2.2 Flash Flood Inundation Characteristics

To obtain the inundation characteristics of flash floods, this study coupled the semi-fractional Hydrologic Engineering Center−Hydrologic Modeling System (HEC-HMS) hydrologic model with the FLO-2D hydrodynamic model. The HEC-HMS model is used for the acquisition of numerical watershed parameters and the calculation of direct runoff. The HEC-HMS model can be used to simulate runoff processes in basins where hydrological data are lacking, similar to the Yantanxi River Basin. The FLO-2D model is a two-dimensional dynamic model based on continuity and motion equations and, combined with flood rheological characteristics (O’Brien et al. 1993 ), the current model is mainly used to simulate surface flooding and debris flow inundation characteristics of plains, creeks, alluvial fans, rivers, or other artificial channels (Peng and Lu 2013 ; Mishra et al. 2018 ). The inundation extent, inundation depth, flow velocity, and impact force characteristics of flash floods can be expressed in the defined grid through the FLO-2D.

The HEC-HMS and FLO-2D coupling steps are: (1) The digital elevation model (DEM) was applied on the Geo-HMS tool in the ArcGIS platform to analyze the filling, flow direction, confluence, and basin division aspects and obtained a basin digital model. (2) Through application of the basin digital model and substitution of the rainfall sequence into the HEC-HMS model, we defined simulation start and end times to obtain the hydrological characteristics of the basin. (3) Clear water flow process lines were input, as retrieved from the HEC-HMS model, flood outflow areas were defined in the FLO-2D model, and then we obtained the flood inundation area, inundation depth, impact force, and flood velocity after setting the simulation time and time step.

2.3.3 Exposure Assessment of the Yantanxi River Basin

Exposure analysis is an important basis for disaster risk assessment. First, the unit of analysis needs to be determined. Second, we need to determine the type and the amount of assets that are exposed to flash floods. Due to the limitations of the current data, this study analyzed the exposure characteristics of the study area based on county-level statistics. The PD, GD, PG, and PA indicators can be obtained through spatial analysis in ArcGIS. The asset value was defined as total areal exposure value, which serves as a mixed economic indicator to maximize the portrayal of disaster damage. The spatial distribution of asset values in the future i -th year can be predicted by the coefficient M that indicates the relationship between GDP and asset value in 2019. In this study M = 3.5.

Socioeconomic data (population and GDP) with a spatial resolution of 0.5°are still too coarse for the river basin scale analysis. Therefore, they need to be corrected. In this study, we used a linear correction process based on spatial analysis in ArcGIS and the county-level statistical data of 2005−2019, and keep the estimation error only within the county units (Zhao et al. 2017 ).

where \({SE}^{{^{\prime}}}\) is the linearly corrected socioeconomic value, \({SE}_{i}\) is the predicted values for each grid, \(SE\) is the historical statistical values, and \({SE}_{ county}\) is the predicted socioeconomic element at the county level.

2.3.4 Risk Assessment of Flash Floods

Vulnerability curves for the study area were obtained from the literature—the questionnaire survey data on natural hazards and disasters were generated by Liu ( 2011 ) for Shuitou Town (located to the southwest of Yongjia County), Wenzhou City, which has a similar location to Yantan Town. We screened the data to obtain 10 samples, and constructed a damage model based on the SPSS platform to predict economic losses from flash floods in the Yantanxi River Basin. The R 2 of the damage model is 0.52, and the expression is:

where L is the damage rate of flood disaster, 0 ≤ L ≤ 100; and D is the inundation depth, D ≥ 0.

2.3.5 Geographical Detector

The geographical detector provides the technical method for the spatial differentiation of geographic elements and reveals the driving forces behind regional risk changes (Wang et al. 2010 ; Wang et al. 2016 ; Wang and Xu 2017 ). Through the geographical detector we predicted the contribution of the flash flood impact factors ( x ) to the risk change ( y ), and the contribution rate is measured by q :

where h = 1,2,..., L is the grading of the independent variable x , and \({N}_{h}\) and \(N\) are the number of samples at level h and the entire area. The \({\sigma }^{2}\) and \({\sigma }_{h}^{2}\) indicate the variations in the entire area and level h , respectively. The value range of q is 0 to 1, and a larger value of q indicates a higher contribution rate of the x to the y (Wang et al. 2010 ; Wang and Xu 2017 ).

The geographical detector requires the input independent variable to be a discrete type quantity, so the results of the study need to be discretized according to a defined hierarchy. The natural break method is often used in the process of disaster risk assessment (Shi 2016 ). The method considers the existence of breakpoints in the array, while maximizing the similarity within each group and maximizing the difference between the classes:

where SSD means variance, i and j refer to the i -th and j -th elements, A refers to an array of length N , K indicates the K -th element in group A , and the K value ranges between i to j .

We used the hazard assessment model and vulnerability model presented in the previous section to predict the change in risk of flash floods with same probability of occurrence, and analyzed the driving factors of the risk change.

3.1 Risk Assessment of Flash Floods

As described in Sect. 2.1 , the Yantanxi River Basin experienced heavy rainfall from 0:00 on 19 July to 11:00 on 20 July 2005 through Super Typhoon Haitang, and the process rainfall records of the 2005 flash flood disaster event from the six meteorological stations are shown in Fig. 2 . In the 2005 flash flood disaster event, the process rainfall at Yongjia station was 328.76 mm (83a), and the rainfall at the Yueqing station was the most extreme, reaching 394.44 mm (120a). Considering the results of the meteorological station weighting and the definitions in 2.3, the return period of the 2005 flash flood was estimated at approximately 83 years. Under the RCP4.5 and RCP8.5 scenarios, the rainfall with the same RP at the Yongjia station is 405.11 mm and 483.96 mm, respectively, with an increase of 76.35 mm and 155.2 mm. The other five meteorological stations had an average increase of 23.94% and 49.46% in the same RP rainfall (Table 2 ). Then, the hourly rainfall assignments for future rainfall events were made based on historical rainfall processes.

Rainfall process of the 2005 flash flood disaster in the Yantanxi River Basin, Southeastern China

Based on the assumption that the surface characteristics remain unchanged, the flash flood hazard under different scenarios can be expressed through FLO-2D. The results show that the change in flash flood inundation extent is not significant, and the hazard is increasing with the same probability of the disaster event. The flash flood inundation area under the future scenario (2052−2100) increases over the historical scenario (2005), which attains an inundation area of 36.44 km 2 and increases by 3.41% and 7.10% under the RCP4.5 and RCP8.5 scenarios, respectively. The area with an inundation depth ranging from 2.00–3.00 m under the RCP4.5 scenario is 8.26 km 2 or 71.32% larger than that under the historical scenario. The area with an inundation depth ranging from 3.00–4.00 m also changes under the RCP8.5 scenario, and is larger than that under the historical scenario by about 87.78%. Moreover, considering an inundation depth greater than 4.00 m, the area under the future scenarios increases 0.11 km 2 and 3.35 km 2 , respectively. Under the historical, RCP4.5, and RCP8.5 scenarios, the percentages of areas with inundation depth > 2.00 m are 8.59%, 25.32%, and 38.36% respectively, while the percentages of areas with inundation depth < 2.00 m are 91.14%, 74.68%, and 61.64%, respectively (Fig. 3 ).

Flash flood inundation change under historical (2005) and future (2052−2100) scenarios in the Yantanxi River Basin, Southeastern China

Considering the regional exposure characteristics, the flash flood risk assessment results indicate that a total asset value of CNY 0.73 billion (2019 prices) was exposed to the 2005 flash flood disaster event. Based on the predicted recurrence time of the 2005 disaster event, the asset value can be calculated through the GDP in 2088, to obtain the damage caused by flash floods under future scenarios. Under the RCP4.5-SSP2 and RCP8.5-SSP5 scenarios, the exposed asset values are expected to reach CNY 7.36 billion and CNY 13.85 billion, respectively. We estimated the economic damage in the 2005 flash flood disaster event was CNY 0.13 billion. Under the RCP4.5-SSP2 and RCP8.5-SSP5 scenarios, the economic damage due to flash floods is expected to reach CNY 1.16 billion and CNY 2.94 billion, respectively, and the risk of flash flood increases by 88.79% and 95.57%, respectively. The proportion of economic damage in the areas with inundation depth > 2.00 m under the historical, RCP4.5-SSP2, and RCP8.5-SSP5 scenarios is 9.37%, 45.77%, and 70.03%, respectively, while the proportion of economic damage of inundation depth < 2.00 m areas is 90.63%, 54.23%, and 29.97%, respectively (Fig. 4 ).

Change of flash flood disaster risk under the historical (2005), RCP4.5-SSP2 (2052−2100), and RCP8.5-SSP5 (2052−2100) scenarios in the Yantanxi River Basin, Southeastern China

3.2 Analysis of the Driving Factors of Change in Flash Flood Risk

This study examined the variation of risk driving factors in different areas through a simple 100 random sampling, and the mean contribution of driving factors is shown in Fig. 5 . Among the hazard indicators, process rainfall (PR) has the largest contribution rate, with 0.59, 0.79, and 0.85 under the historical, RCP4.5-SSP2, and RCP8.5-SSP5 scenarios, respectively, with an increasing trend in the contribution to flash flood risk. The contribution of impact force (IF) to risk shows a fluctuating decrease with an average contribution of 0.26. The contribution of flow velocity (FV) to changes in flash flood risk is the weakest, with an average contribution of 0.10. Impact force and flow velocity correlate with changes in surface features in the study area. Among the exposure indicators, the contributions of GDP density (GD) and asset density (AD) to risk are significantly prominent, with the contribution of GD being 0.84, 0.82, and 0.89 under the three scenarios, respectively, which is better than the other indicators to explain the risk variation characteristics of flash floods in the Yantanxi River Basin. As the population decreases under the future scenario, the contribution of population density (PD) to risk tends to decrease to 0.74, 0.72, and 0.56, respectively. The development of socioeconomic factors promotes the increase of regional inputs and the strengthening of regional protection capacity, thus the contribution of per capita asset value (PA) to risk shows a significant decreasing trend, and its contribution is 0.59, 0.53, and 0.20 under the historical, RCP4.5-SSP2, and RCP8.5-SSP5 scenarios, respectively. The mean contribution of per capita GDP (PG) is 0.64. In general, the natural and socioeconomic factors combine to increase the risk of flash floods in the Yantanxi River Basin. According to the mean contribution rate of each indicator under the three scenarios, the main factors that affect the flash flood risk change in the basin are GD ( q = 0.85) > PR ( q = 0.74) > AD ( q = 0.68) > PD ( q = 0.67).

Contribution rate of the eight indicators to flash flood risk in the Yantanxi River Basin, Southeastern China. FV Flow velocity, IF Impact force, PR Process rainfall, PD Population density, GD GDP density, AD Asset density, PG Per capita GDP, PA Per capita asset value

4 Discussion

In this study, based on the principles of disaster risk assessment, we identified the flash flood risk assessment indicators and used them under a combination of the RCP and SSP scenarios to obtain the changes of flash flood risk in the Yantanxi River Basin in the context of climate change, and predicted the contributions of the driving factors behind the risk change.

Under the RCP4.5-SSP2 and RCP8.5-SSP5 scenarios, the area affected by flash floods is increasing in the Yantanxi River Basin, which is consistent with the characteristics of flash floods in other river basins (Chen et al. 2019 ; Yang et al. 2020 ). The NEX-GDDP dataset downscales the rainfall of future scenarios by means of bias correction, which better reflects the Chinese rainfall characteristics and can be better applied to the analysis of the rainfall characteristics at the river basin scale (Chen et al. 2017 ; Zhang et al. 2019 ; Li et al. 2020 ). Inevitably, after downscaling, there are still internal errors in the choice of parameters within the climate model (Chen et al. 2017 ). In addition, the effect of the elevated topography in the region is such that the climate model underestimates the extreme precipitation characteristics of the region (Lafon et al. 2013 ). To this end, considering the impact of tropical cyclones on rainfall in the study area, this study used tropical cyclone-induced rainfall as a representative of extreme precipitation in the region, the rainfall characteristics of six meteorological stations were extracted through spatial and temporal element analysis (Fang et al. 2011 ), and the impact of these characteristics were added for future scenarios. Because rainfall is the main trigger of flash floods, the corrected rainfall data provide the basis for flash flood risk assessment. To better reflect the changes of flash flood risk with the same disaster event probability, this study assigned future scenario rainfall according to the historical rainfall process. Due to the lack of data on hydrological elements, we coupled the HEC-HMS and FLO-2D models to obtain the characteristics of flash floods under different scenarios. Based on the assumption of constant subsurface characteristics, the impact areas of flash floods in the Yantanxi River Basin increased by 3.41% and 7.10% under the RCP4.5-SSP2 and RCP8.5-SSP5 scenarios (2052−2100), respectively, compared with the historical scenario (2005). Differences in characteristics—such as the location of the study area, the characteristics of regional rainfall, selection of climate model data, model downscaling methods, subsurface, and hydrological elements—can lead to different predicted risk results (Hurkmans et al. 2010 ; Lafon et al. 2013 ; Dahm et al. 2016 ).

Under the future scenarios (2052−2100), the risk of flash floods in the Yantanxi River Basin increases by 88.79% and 95.57% compared with the historical scenario (2005). According to the definition of flash flood risk, the change in its risk is expressed quantitatively in terms of economic damage (Shi 2016 ). First, considering the principle of similarity of location, we selected the disaster survey data and constructed the vulnerability curves of flash floods in the Yantanxi River Basin, while introducing the asset value to maximize the portrayal of economic damage caused by flash floods (UNDRR 2013 ; Wu et al. 2014 ; Li et al. 2017 ). Nanjing University of Information Science and Technology (NUIST) localized SSP data after considering the characteristics of Chinese socioeconomic elements, which provides data support for climate change risk studies at regional and river basin scales (Jiang et al. 2018 ; Huang et al. 2019 ). In order to reduce the variation in spatial resolution and keep the error in the results only within the region, the socioeconomic characteristics of the future scenarios were corrected for the relationship between the statistical data and the forecast data (Zhao et al. 2017 ). Based on the corrected SSPs, we predicted the flash flood risk for future scenarios under the assumption of constant vulnerability of the exposed assets. Differences in disaster exposure values (such as GDP, housing replacement cost, and so on), the change of region vulnerability, and correction methods of socioeconomic data will lead to differences in the results of risk assessment (Dutta et al. 2003 ; Pistrika et al. 2014 ; Armal et al. 2020 ).

Based on the constructed flash flood risk assessment indicator system, the driving factors for the change of flash flood risk are GD > PR > PD > AD. The risks in this study are expressed in terms of economic damage, the changes of which are closely related to the development of GDP. The geographical detector provides a reference for detecting the spatiotemporal heterogeneity of geographical things, and further reveals the characteristics of the driving factors behind the risk changes (Wang et al. 2010 ; Wang et al. 2016 ; Wang and Xu 2017 ). Geographical detector analysis requires discrete variables. The natural break method in disaster risk assessment was used to discretize the independent and dependent variables in this study. Liu et al. ( 2018 ) used the geographical detector to analyze the spatial distribution characteristics of flash flood disasters in China and pointed out that the main driving factor is precipitation. In their research, 11 ecological zones in China were taken as the research area, and the indicators, including rainfall and human activities, were selected to detect their driving effects on historical flash flood events in China from 1951−2015 (Liu et al. 2018 ). One of the reasons why their findings differ from this study is that their variables were discretized by the centroid comparison method, and the system of indicators chosen also differed from this study. Thus, the resulting final detected impact factors are different from our research. Therefore, factors such as research scale, risk expression, and the choice of the factor discretization method can lead to differences in the detected impact factors (Wang and Xu 2017 ; Gusain et al. 2020 ; Rong et al. 2020 ).

There are limitations in our study and improvements can be made in three aspects:

Considering more factors that affect the extreme rainfall to improve the assessment of hazard changes in the study area. Although based on the existing research, there are still errors with observed precipitation due to the large uncertainties in tropical cyclones and the difficulties in the simulation of TCs and their impacts by climate models (Sobel et al. 2016 ; Ye et al. 2019 ). In future studies, the factors that affect extreme rainfall such as topography and regional circulation should also be considered, in order to improve the accuracy of regional extreme rainfall prediction (Sun et al. 2015 ). Based on the available data, this study used meteorological station data to analyze the rainfall characteristics in the study area, and the errors arising from the results would be greater than the results from gauge stations. Moreover, we selected only a single climate model with better simulation ability in China and carried out our analysis of precipitation characteristics under future scenarios using this model. Chen et al. ( 2020 ) used CMIP3 and CMIP5 data to investigate the changing characteristics of typhoon–rainfall–landslide features in Taiwan in the context of climate change. Their findings show that the ensemble scenarios method can minimize the uncertainty in the assessment results compared with each individual model, and the most extreme change features can be identified (Chen et al. 2020 ). In future studies, the ensemble method can be used to simulate regional extreme rainfall and the effect on flash floods (Mishra et al. 2018 ; Goodarzi et al. 2019 ).

Considering the impact of the surface characteristics on the flash flood risk assessment. In this study, we developed the simulation of flash flood hazard under the future scenarios based on the assumption that subsurface characteristics remain unchanged, which increases the uncertainty of flash flood risk assessment. Lin et al. ( 2020 ) elaborated on an approach that employs a future land-use simulation model (FLUS) for 100a coastal flood risk assessment, and the results indicate that there is a significant contribution of subsurface changes to flooding hazard. In future studies, we can predict the changes of surface features such as random forest by machine learning algorithms, so that the impact of surface features on flash flood risk can be incorporated (Deshmukh et al. 2013 ; Wang et al. 2021 ).

The method of risk assessment needs to be improved. In this study, vulnerability curves were constructed based on the town-scale flood disaster survey of Wenzhou City in 2011, and correction of the vulnerability curve was lacking in the risk assessment of future scenarios. At the same time, the sample size of disaster data has an impact on the accuracy of damage predictions. In addition, this study used asset values as the characteristics of exposure and predicted asset value under future scenarios based on the relationship coefficient, without considering the changes of the area. In future studies, the vulnerability curve can be updated by means of a local survey, or improved by appropriate parameter correction (Pistrika et al. 2014 ; Zhang et al. 2021 ). With the improvement of disaster database construction and spatialization methods, the prediction of regional exposure value can be improved (Ma et al. 2014 ; Chen and Nordhaus 2015 ; Wu et al. 2018 ). Since this study focused on the application of natural hazard risk assessment methods in the context of climate change, the assessment results contain many uncertainties. The uncertainty in the assessment results can be quantified by probability theory in future studies (Yi et al. 2014 ).

5 Conclusion

Using the publicly available data and based on existing research, this study conducted a flash flood risk assessment under climate change in the Yantanxi River Basin of southeastern China. The results show that compared to the historical scenario (2005), the areas affected by flash floods increased by 3.41% and 7.10% respectively under the RCP 4.5 (2052−2100) and RCP 8.5 (2052−2100) scenarios, which is not a very significant change. In addition, there is a decreasing trend for the areas with an inundation depth below 2 m, whereas the areas with inundation depth greater than 2 m exhibit an increasing trend. The risk of flash floods with the same probability of occurrence is increasing under climate change. This study constructed the vulnerability curves based on the 2011 flood disaster survey data. Under the assumption of constant vulnerability of the exposed assets, the risk of flash flood disaster increases by 88.79% and 95.57% respectively under the RCP 4.5-SSP2 and RCP 8.5-SSP5 scenarios, compared to the historical scenario. Socioeconomic factors are the main drivers of change in flash flood risks. The geographical detector analysis result shows that the main factors that affect the change of flash flood risk are GDP density, process rainfall, asset density, and population density.

The assessment results highlight that the changes in climatic and socioeconomic conditions increase the risk of flash floods. For people living in areas affected by flash floods, there is a need to increase education and awareness of flash flood precautions. Our findings suggest that socioeconomic development will boost regional disaster prevention capacity, but at the same time drive the increase in flash flood risk. Considering these impacts, balancing economic growth, risk management, and risk avoidance is an important issue that needs to be addressed in the long-term development of the area, which is strongly supported by tourism.

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Acknowledgements

We would like to thank the editors and reviewers for their comments. We also would like to express our gratitude to Professor Jidong Wu’s team at Beijing Normal University, and Professor Tong Jiang’s team at Nanjing University of Information Science and Technology for providing data support. This work was supported by the National Key Research and Development Program (2017YFA0604903, 2017YFC1502505).

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Chen, L., Yan, Z., Li, Q. et al. Flash Flood Risk Assessment and Driving Factors: A Case Study of the Yantanxi River Basin, Southeastern China. Int J Disaster Risk Sci 13 , 291–304 (2022). https://doi.org/10.1007/s13753-022-00408-3

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What Is Flash Flooding, and How Can You Avoid It?

By Neil Vigdor

Flash floods are so named because sudden deluges can occur after a heavy rainfall, which the National Weather Service says is the most common cause. The flooding begins within six hours of heavy rain and often within three hours of an intense rainfall, though sometimes it can happen within minutes, giving people little time to take precautions.

Flooding occurs in areas where the ground is unable to absorb all of the water that has fallen, according to forecasters, who explained that flash flooding can also be caused by mudslides or breaks in dams or levees.

Urban areas are particularly vulnerable to flash flooding because they have a lot of paved surfaces.

A flash flood warning means that flash flooding is imminent or already happening, under the designations used by the Weather Service . A flash flood watch indicates that conditions are favorable for flash floods and that they are possible.

Flash floods are distinct from floods, which the Weather Service defines as inundation of a normally dry area with rising water from a river or stream. Flooding can last days or weeks, which is much longer than flash flooding.

Flash flooding is dangerous, in part because appearances can be deceiving.

“Six inches of fast, flowing water can knock you over, and two feet is enough to float an entire vehicle,” said Katie Wilkes, a spokeswoman for the American Red Cross. “I think one of the most important things to know is that flash floods are called flash floods for a reason.”

The Red Cross recommends that people closely monitor weather forecasts for flash flooding advisories, keep an emergency kit at hand and develop an evacuation plan. Move immediately to higher ground, and never try to cross floodwaters.

The driver of a vehicle that is on a flooded road should get out and move to higher ground if it is safe, Ms. Wilkes said. The Weather Service cautions that roadbeds may be washed away beneath floodwaters, making them dangerous to drive through.

More people die each year in the United States as a result of flash flooding than from tornadoes, hurricanes or lightning, according to the Weather Service.

Assessing the dangers of floodwaters can be even more difficult at night, according to state and federal public safety agencies, which warn people to avoid camping or parking next to creeks or in other flood-prone areas.

Neil Vigdor covers political news for The Times. More about Neil Vigdor

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Predicting Responses to Flash Flooding : A Case Study of Boulder, Colorado

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What causes flash floods?

Find out what causes flash flooding and why it can be so dangerous.

What are flash floods?

Flash flooding happens when rain falls so fast that the underlying ground cannot cope, or drain it away fast enough. Roads can become like rivers and if there is a lot of water, it can flood buildings and carry cars away. So, if the rain is falling too fast for the ground or drains to cope, there is a risk of flash flooding.

Most rivers flow fairly gently as they slope slowly towards the sea. Therefore, when a river floods it does so quite slowly as it takes time for the rain to percolate through the ground and into the rivers and out to sea, allowing time for some warning. With flash flooding, there is often very little time between the rain falling and flash flooding occurring.

Flash flooding commonly happens more where rivers are narrow and steep, so they flow more quickly. It can also occur from small rivers in built-up urban areas, where hard surfaces such as roads and concrete don't let the water drain away into the ground. This leads to surface overflow and can often overwhelm local drainage systems, leading to flash flooding.

Case study: Boscastle floods in 2004

The village of Boscastle in Cornwall experienced flash flooding on 16 August 2004 when an exceptional amount of rain fell over eight hours. Heavy thundery showers developed over the moors to the southwest of Boscastle. A small, steep river flows through Boscastle and the thundery showers came one after another in a short space of time, over the same spot at the head of the river. There was simply too much water for this small river and the surrounding hillside to cope with, and the water gushed rapidly down the steep slopes into Boscastle .

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5 facts about flash floods after they cripple northeast towns.

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Old Sawmill Miniature Golf Course in Oxford, Connecticut, was destroyed by Sunday’s flooding. “This ... [+] is our family business of 30 years,” said Joslynn Turcott, daughter of owner Al Turcott, who built the miniature golf course from the ground up. Joslynn said that like most other businesses in Oxford, they don’t have flood insurance.

The recent catastrophic flooding in Connecticut and Long Island, New York serve as a stark reminder of the destructive potential of rapid-onset floods. These events were the result of a perfect storm of meteorological conditions. A stationary frontal boundary combined with a strong low-pressure system blocked in place by Hurricane Ernesto created an ideal environment for sustained heavy rainfall. This phenomenon led to multiple rounds of intense showers and thunderstorms concentrated in the same area, ultimately resulting in devastating flooding.

These RadarScope images show intense rainfall over Connecticut and Long Island.

Unfortunately, this dangerous scenario is expected to happen more frequently. A recent study published in Communications Earth & Environment forecast flash flooding to increase by nearly 8% in the next 75 years with “future flash flood-prone frontiers.” Faced with this increasing weather risk, here are some things you should know about flash floods.

What Are Flash Floods?

Flash floods are a sudden and powerful surge of water. They differ from regular flooding in the speed of onset, duration and impact. Flash floods develop very quickly, often within minutes to a few hours. They are short-lived, lasting from a few hours to a day, depending on the intensity of the rainfall or the source of water. Regular floods develop more slowly and can last for days, weeks or even months, particularly if they are caused by prolonged rainfall, river overflow or sustained storm surges.

This sudden onset is what makes them particularly dangerous, as they leave little time for warning or evacuation. In contrast, regular floods, such as river or coastal floods, develop more slowly, often over several days or even weeks, providing more time for people to prepare and respond.

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Although flash floods can happen almost anywhere, certain spots are particularly vulnerable. Areas with steep terrain, such as mountainous regions, are more prone to flash floods because water flows downhill quickly, concentrating in narrow valleys and low-lying areas. For instance, in 2022, the Appalachian region experienced severe flash flooding due to a combination of heavy rainfall and steep topography. The floods resulted in 39 fatalities and extensive property damage.

Urban areas are also vulnerable to flash flooding. The prevalence of impervious surfaces like roads, parking lots and buildings prevents rainwater from being absorbed into the ground, leading to rapid runoff and overwhelmed drainage systems. This issue is exacerbated by the lack of green spaces that would otherwise absorb and slow down the flow of water.

It's important to note that proximity to large bodies of water is not a prerequisite for flash flooding. Even areas far from rivers or coastlines can experience these sudden and destructive events under the right conditions.

Old Sawmill Miniature Golf Course in Oxford, Connecticut was destroyed by Sunday’s flooding.

How Strong Are Flash Floods?

The speed and volume of water involved in flash floods give them the power to carry large debris, such as rocks and tree trunks, further amplifying their destructive potential. For instance, during the 2013 flash floods in Colorado , water surged down steep canyons with such intensity that it washed away entire roads and bridges, isolating communities for days. Flash floods are also capable of carving out new channels, altering landscapes permanently.

Are Flash Floods Increasing?

Flash floods have become an increasingly common natural disaster in recent years because climate change exacerbates the risk. As the atmosphere warms, it can hold more moisture, leading to more intense rainfall events. I explain this more in an earlier article in Forbes , with hurricanes adding an additional threat of heavy precipitation. The most recent example is Hurricane Debby, which dumped up to 21 inches of rain in North Carolina.

Studies, such as one published in Natural Hazards in 2021, show that urban expansion combined with extreme weather events significantly increases the likelihood of flash floods.

Are There Signs To Watch For Potential Flash Flooding?

Preparing for flash floods involves a combination of staying informed, planning ahead and taking practical measures to minimize risks. One of the most critical steps is staying informed through weather alerts and understanding the flood risk in your area by checking local flood maps. Knowing whether you live in a flood-prone region, such as near rivers or low-lying areas, can help you take appropriate precautions.

Flash floods leave little time for warning or evacuation. Create an emergency plan that includes clear communication strategies with your family, identifying safe evacuation routes and establishing a meeting point in case you get separated. Equally important is preparing an emergency kit that includes essentials like food, water, medications and important documents, ensuring you have what you need if you need to evacuate quickly.

Reducing Flash Flooding Risks For The Future

As climate change continues to influence weather patterns, it’s increasingly crucial to understand and prepare for flash floods. By staying informed about weather conditions and recognizing the warning signs, communities can better protect themselves against this growing natural hazard.

The increasing frequency and intensity of these events underscore the need for improved urban planning, enhanced drainage systems and greater public awareness to mitigate the potentially devastating impacts of flash floods. Their strength is not only in physical destruction but also in their ability to disrupt entire communities, causing long-term economic damage.

On a promising note, detailed flood modeling and greater computational power may provide more advanced warnings. Improved models reduce computational time from days to hours in large areas, and less than an hour in smaller areas. This will give city officials, emergency crews, the public and businesses more time to prepare and evacuate from these raging waters.

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How satellites could protect archaeology sites vulnerable to climate change

Satellite studies "could provide valuable insights into these impacts."

Archaeologists should consider tapping into satellite imagery when assessing the impacts climate change can have on ancient underground structures, a new study argues.

Satellite studies would be one tool to help monitor and stabilize "underground heritage sites" on Earth that are becoming more vulnerable to human-induced climate change , flash floods, urbanization and other threats, the Aug. 12 study in Heritage Science states .

Specifically, the study focuses on the ancient city of Alexandria, Egypt. This area is perhaps best known for the famous Lighthouse (or Pharos) of Alexandria, which was completed around 280 BCE during the reign of Ptolemy II, according to Britannica . 

The lighthouse was said to be one of the seven ancient wonders of the world — while no longer standing, it is just one example of the archaeological potential of Alexandria. Climate change, however, is affecting subterranean Greek, Roman and Egyptian tombs and necropolis in the region as these structures were built in porous limestone (calcarenitic) rocks that are vulnerable to water.

Related: Four amazing astronomical discoveries from ancient Greece

"Climate change is increasing the number of natural disasters like flash flooding, heavy rain and sea level rise and their intensive negative impact on UNESCO world natural and cultural heritage sites in Alexandria," the study states. (UNESCO is the United Nations Educational, Scientific and Cultural Organization and maintains a list of heritage sites around the world .)

"Recently, the increasing number of natural disasters linked to climate crises has put Alexandria's underground and above surface cultural built heritage at greater risk than ever before, which imposes new complex challenges on us to safeguard the historical urban fabric and built heritage in Alexandria," the study authors added.

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Study author Sayed Hemeda presents a case study of damage imparted on the 2nd century AD Catacombs of Kom El-Shoqafa; in fact, the researcher had already examined the site across several peer-reviewed studies prior to this one. Hemeda is a professor with Cairo University's faculty of archaeology, focusing on architectural conservation.

Lonely Planet describes the site as "one of the last major works of construction dedicated to the religion of ancient Egypt," which is important as it shows how Pharaonic and Greek styles were blended roughly 1,800 years ago. The bulk of Hemeda's study discusses possible methods to stabilize the rock when flooding and similar climate change-induced problems occur. But the implications are wide-ranging, as the catacombs' issues illustrate the importance of protecting other ancient sites vulnerable to global warming as well.

 —  NASA searches for climate solutions as global temperatures reach record highs

— Climate change keeps space debris afloat longer

— Moon-dust shield could help fight climate change on Earth

Satellites have various tools to track structures and movements beneath the Earth, such as ground-penetrating radar, to see old buildings or monuments, and gravity-based groundwater measurements to track water. (NASA's now-retired Gravity Recovery and Climate Experiment or GRACE is a famous example of twin satellites using gravity to examine water underground.)

While satellite images can be expensive to obtain, open-access and older imagery have been used many times in recent peer-reviewed literature. Hemeda cites a December 2023 study of archaeological landscapes in Egypt's Nile Delta as an example of good dataset usage from archives. That study's data came from instruments including Landsat (a joint effort of NASA and the U.S. Geological Survey), the decommissioned spy satellite series called Corona, and the Advanced Spaceborne Thermal Emission and Reflection Radiometer instrument aboard NASA's still-active Terra satellite.

As a whole, satellite studies may assist, Hemeda says, in examining "the broader impact of climate change on archaeological sites, particularly in vulnerable areas like the northern Nile Delta."

Join our Space Forums to keep talking space on the latest missions, night sky and more! And if you have a news tip, correction or comment, let us know at: [email protected].

Elizabeth Howell (she/her), Ph.D., is a staff writer in the spaceflight channel since 2022 covering diversity, education and gaming as well. She was contributing writer for Space.com for 10 years before joining full-time. Elizabeth's reporting includes multiple exclusives with the White House and Office of the Vice-President of the United States, an exclusive conversation with aspiring space tourist (and NSYNC bassist) Lance Bass, speaking several times with the International Space Station, witnessing five human spaceflight launches on two continents, flying parabolic, working inside a spacesuit, and participating in a simulated Mars mission. Her latest book, " Why Am I Taller ?", is co-written with astronaut Dave Williams. Elizabeth holds a Ph.D. and M.Sc. in Space Studies from the University of North Dakota, a Bachelor of Journalism from Canada's Carleton University and a Bachelor of History from Canada's Athabasca University. Elizabeth is also a post-secondary instructor in communications and science at several institutions since 2015; her experience includes developing and teaching an astronomy course at Canada's Algonquin College (with Indigenous content as well) to more than 1,000 students since 2020. Elizabeth first got interested in space after watching the movie Apollo 13 in 1996, and still wants to be an astronaut someday. Mastodon: https://qoto.org/@howellspace

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Western states have seen an uptick in water-related deaths

Flash flooding, strong winds have been factors in several of the incidents.

By McKenna Jensen

Western states have seen several water-related tragedies in recent weeks, including three Utah hikers who lost their lives in California on Friday.

Some of the deaths were related to the late summer monsoons and the flash flooding that follows.

Grand Canyon flash flood

Heavy rain hit the Colorado River valley last Wednesday initiating a flash flood that took the life of one hiker and left several others stranded on Thursday. The National Park Service said the body of Chenoa Nickerson was recovered following the flash flood. It was reported that Nickerson was swept into the water and was found by a commercial river trip three days after she went missing.

“The resulting runoff moves rapidly through the narrow canyons and steep terrain found throughout the region. In many areas, even small storms can turn normally dry streambeds into raging torrents of water in a matter of minutes,” per KTNV Las Vegas.

Sierra Nevada whirlpool

In the Sierra Nevada Mountain range three Utahns drowned after being caught in a whirlpool while rappelling near Tulare County, California, last Friday afternoon, according to the Tulare County Fire Department. David Bell, Jeannine Skinner and Peter On were participating in a canyoneering adventure exploring the Seven Teacups area, KSL reported . According to a Facebook post written by Bell’s sister, Cyndi Miller, the three were the last of the group to rappel when Skinner was caught in a whirlpool, leading On then Bell to attempt a rescue. All three drowned.

Quail Creek State Park paddle-boarding accident

Another incident took place on Friday, when authorities reported a man in his 40s died after a paddle-boarding accident at Quail Creek State Park in Hurricane. According to KSL News Radio, Utah State Parks said the man was paddle-boarding with two others when heavy winds picked up causing them to fall into the reservoir. Two of the individuals resurfaced quickly, while the man remained underwater for less than a minute. Emergency responders came to aid along with a member of the public and a Utah Division of Outdoor Recreation ranger. They began CPR on the boat ramp and Life Flight was on their way. Unfortunately, the man was pronounced dead at the scene. Officials have yet to determine if his death was due to drowning or a medical incident, and the case remains under investigation.

One dead, one missing in Flaming Gorge

Over the weekend a drowning incident occurred at the Flaming Gorge Reservoir in Daggett County. The Daggett County Sheriff confirmed a 60-year-old women died and her 44-year-old daughter is still missing. According to the sheriff’s office a 911 call was placed regarding a possible drowning at Swim Beach on Saturday. The call initiated emergency services including Flaming Gorge Ambulance, Green River Fire Department, Castle Rock Ambulance, AirMed and Life Flight. Upon arriving at the scene deputies found an adult male performing CPR on the 60-year-old woman and another female witness who was the adult daughter of the 60-year-old . They advised authorities that another 44 year-old female was still missing.

The 60-year-old woman was pronounced dead and the 44-year-old woman was still missing. Search teams returned Sunday morning to continue looking for the missing woman.

Water safety

According the the American Red Cross , “It only takes a moment. A child or weak swimmer can drown in the time it takes to reply to a text, check a fishing line or apply sunscreen. Death and injury from drownings happen every day in home pools and hot tubs , at the beach or in oceans , lakes, rivers and streams , bathtubs, and even buckets.”

Here is a few key points the American Red Cross highlights to keep you and loved ones safe from the dangers of water:

• “Know your limitations, including physical fitness, medical conditions.
• Never swim alone; swim with lifeguards and/or water watchers present.
• Wear a U.S. Coast Guard-approved life jacket appropriate for your weight and size and the water activity. Always wear a life jacket while boating, regardless of swimming skill.
• Swim sober.
• Understand the dangers of hyperventilation and hypoxic blackout. Know how to call for help.

Understand and adjust for the unique risks of the water environment you are in, such as:

• River currents.
• Ocean rip currents.
• Water temperature.
• Shallow or unclear water.”

To read more about water safety check out the American Red Cross website .